Overview

Dataset statistics

Number of variables19
Number of observations24728
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 MiB
Average record size in memory181.4 B

Variable types

Numeric9
Categorical9
Boolean1

Alerts

Time_Order_picked_hour has constant value ""Constant
Time_Order_picked_minute has constant value ""Constant
Restaurant_latitude is highly overall correlated with Delivery_location_latitudeHigh correlation
Restaurant_longitude is highly overall correlated with Delivery_location_longitudeHigh correlation
Delivery_location_latitude is highly overall correlated with Restaurant_latitudeHigh correlation
Delivery_location_longitude is highly overall correlated with Restaurant_longitudeHigh correlation
Time_Orderd_hour is highly overall correlated with Road_traffic_densityHigh correlation
Road_traffic_density is highly overall correlated with Time_Orderd_hourHigh correlation
Festival is highly imbalanced (85.9%)Imbalance

Reproduction

Analysis started2023-04-23 12:14:58.118356
Analysis finished2023-04-23 12:15:34.711607
Duration36.59 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Delivery_person_Age
Real number (ℝ)

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.647444
Minimum20
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size902.4 KiB
2023-04-23T17:45:34.846569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile21
Q125
median30
Q335
95-th percentile39
Maximum39
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.7666027
Coefficient of variation (CV)0.1945059
Kurtosis-1.2134084
Mean29.647444
Median Absolute Deviation (MAD)5
Skewness-0.037587693
Sum733122
Variance33.253707
MonotonicityNot monotonic
2023-04-23T17:45:35.129373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
37 1328
 
5.4%
36 1320
 
5.3%
35 1308
 
5.3%
30 1287
 
5.2%
34 1277
 
5.2%
22 1276
 
5.2%
38 1253
 
5.1%
32 1242
 
5.0%
39 1238
 
5.0%
33 1230
 
5.0%
Other values (10) 11969
48.4%
ValueCountFrequency (%)
20 1188
4.8%
21 1163
4.7%
22 1276
5.2%
23 1176
4.8%
24 1194
4.8%
25 1209
4.9%
26 1230
5.0%
27 1190
4.8%
28 1199
4.8%
29 1229
5.0%
ValueCountFrequency (%)
39 1238
5.0%
38 1253
5.1%
37 1328
5.4%
36 1320
5.3%
35 1308
5.3%
34 1277
5.2%
33 1230
5.0%
32 1242
5.0%
31 1191
4.8%
30 1287
5.2%

Delivery_person_Ratings
Real number (ℝ)

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6314057
Minimum2.5
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size902.4 KiB
2023-04-23T17:45:35.458383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile4
Q14.5
median4.7
Q34.9
95-th percentile5
Maximum5
Range2.5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.31692082
Coefficient of variation (CV)0.068428646
Kurtosis4.6613926
Mean4.6314057
Median Absolute Deviation (MAD)0.2
Skewness-1.7353103
Sum114525.4
Variance0.10043881
MonotonicityNot monotonic
2023-04-23T17:45:35.775581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
4.7 4065
16.4%
4.8 4020
16.3%
4.9 3955
16.0%
4.6 3853
15.6%
5 2268
9.2%
4.5 1872
7.6%
4.3 847
 
3.4%
4.2 826
 
3.3%
4.1 807
 
3.3%
4.4 768
 
3.1%
Other values (16) 1447
 
5.9%
ValueCountFrequency (%)
2.5 9
< 0.1%
2.6 12
< 0.1%
2.7 10
< 0.1%
2.8 13
0.1%
2.9 9
< 0.1%
3 5
 
< 0.1%
3.1 15
0.1%
3.2 18
0.1%
3.3 14
0.1%
3.4 21
0.1%
ValueCountFrequency (%)
5 2268
9.2%
4.9 3955
16.0%
4.8 4020
16.3%
4.7 4065
16.4%
4.6 3853
15.6%
4.5 1872
7.6%
4.4 768
 
3.1%
4.3 847
 
3.4%
4.2 826
 
3.3%
4.1 807
 
3.3%

Restaurant_latitude
Real number (ℝ)

Distinct314
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.869995
Minimum-9.988483
Maximum30.905562
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)< 0.1%
Memory size902.4 KiB
2023-04-23T17:45:36.133488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-9.988483
5-th percentile11.016298
Q112.986047
median18.994237
Q322.745049
95-th percentile26.921411
Maximum30.905562
Range40.894045
Interquartile range (IQR)9.759002

Descriptive statistics

Standard deviation5.44133
Coefficient of variation (CV)0.28835885
Kurtosis-0.83110155
Mean18.869995
Median Absolute Deviation (MAD)4.364796
Skewness0.099387426
Sum466617.23
Variance29.608072
MonotonicityNot monotonic
2023-04-23T17:45:36.509159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.911378 139
 
0.6%
26.902328 135
 
0.5%
26.914142 135
 
0.5%
26.902908 132
 
0.5%
26.892312 132
 
0.5%
26.766536 131
 
0.5%
22.308096 130
 
0.5%
12.323978 130
 
0.5%
26.956431 129
 
0.5%
17.431668 129
 
0.5%
Other values (304) 23406
94.7%
ValueCountFrequency (%)
-9.988483 1
 
< 0.1%
-9.982834 2
 
< 0.1%
-9.970717 1
 
< 0.1%
-9.966783 1
 
< 0.1%
-9.960846 1
 
< 0.1%
10.000706 21
0.1%
10.003064 23
0.1%
10.006881 30
0.1%
10.020683 21
0.1%
10.027014 26
0.1%
ValueCountFrequency (%)
30.905562 29
0.1%
30.902872 23
0.1%
30.899992 30
0.1%
30.899584 30
0.1%
30.895817 23
0.1%
30.895204 27
0.1%
30.893384 30
0.1%
30.893244 27
0.1%
30.893081 29
0.1%
30.892978 24
0.1%

Restaurant_longitude
Real number (ℝ)

Distinct314
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.905353
Minimum72.768726
Maximum88.433452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size902.4 KiB
2023-04-23T17:45:36.902514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum72.768726
5-th percentile72.793616
Q173.899315
median76.625861
Q378.375467
95-th percentile85.325146
Maximum88.433452
Range15.664726
Interquartile range (IQR)4.476152

Descriptive statistics

Standard deviation3.4017122
Coefficient of variation (CV)0.04423245
Kurtosis1.5083592
Mean76.905353
Median Absolute Deviation (MAD)1.76753
Skewness1.1556419
Sum1901715.6
Variance11.571646
MonotonicityNot monotonic
2023-04-23T17:45:37.282378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.789034 139
 
0.6%
75.794257 135
 
0.5%
75.805704 135
 
0.5%
75.792934 132
 
0.5%
75.806896 132
 
0.5%
75.837333 131
 
0.5%
73.167753 130
 
0.5%
76.627961 130
 
0.5%
75.776649 129
 
0.5%
78.408321 129
 
0.5%
Other values (304) 23406
94.7%
ValueCountFrequency (%)
72.768726 121
0.5%
72.768778 122
0.5%
72.771477 121
0.5%
72.772629 113
0.5%
72.772697 118
0.5%
72.774209 113
0.5%
72.778666 114
0.5%
72.789122 127
0.5%
72.790489 103
0.4%
72.792731 112
0.5%
ValueCountFrequency (%)
88.433452 26
0.1%
88.433187 30
0.1%
88.400581 21
0.1%
88.393294 28
0.1%
88.368628 25
0.1%
88.366217 25
0.1%
88.365507 23
0.1%
88.364878 20
0.1%
88.364453 26
0.1%
88.362504 26
0.1%
Distinct3417
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.938687
Minimum10.010706
Maximum31.045562
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size902.4 KiB
2023-04-23T17:45:37.703598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10.010706
5-th percentile11.076686
Q113.064365
median19.084237
Q322.807021
95-th percentile27.041378
Maximum31.045562
Range21.034856
Interquartile range (IQR)9.742656

Descriptive statistics

Standard deviation5.426342
Coefficient of variation (CV)0.28652155
Kurtosis-0.99586338
Mean18.938687
Median Absolute Deviation (MAD)4.334796
Skewness0.13391731
Sum468315.85
Variance29.445187
MonotonicityNot monotonic
2023-04-23T17:45:38.058829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.148616 17
 
0.1%
17.568263 17
 
0.1%
17.490827 16
 
0.1%
22.863838 16
 
0.1%
21.227735 16
 
0.1%
27.023483 16
 
0.1%
19.161458 16
 
0.1%
21.197735 16
 
0.1%
19.135838 16
 
0.1%
22.400329 16
 
0.1%
Other values (3407) 24566
99.3%
ValueCountFrequency (%)
10.010706 1
 
< 0.1%
10.013064 2
< 0.1%
10.016783 1
 
< 0.1%
10.016881 3
< 0.1%
10.020706 1
 
< 0.1%
10.023064 3
< 0.1%
10.026881 3
< 0.1%
10.030683 1
 
< 0.1%
10.030706 2
< 0.1%
10.030717 1
 
< 0.1%
ValueCountFrequency (%)
31.045562 3
< 0.1%
31.042872 2
< 0.1%
31.039992 3
< 0.1%
31.039584 3
< 0.1%
31.035817 4
< 0.1%
31.035562 3
< 0.1%
31.035204 3
< 0.1%
31.033384 3
< 0.1%
31.033244 3
< 0.1%
31.033081 2
< 0.1%
Distinct3417
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.969204
Minimum72.778726
Maximum88.563452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size902.4 KiB
2023-04-23T17:45:38.448730image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum72.778726
5-th percentile72.852697
Q173.954337
median76.680583
Q378.407865
95-th percentile85.366967
Maximum88.563452
Range15.784726
Interquartile range (IQR)4.453528

Descriptive statistics

Standard deviation3.4018846
Coefficient of variation (CV)0.044197997
Kurtosis1.5059273
Mean76.969204
Median Absolute Deviation (MAD)1.774362
Skewness1.1546741
Sum1903294.5
Variance11.572819
MonotonicityNot monotonic
2023-04-23T17:45:38.829455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.354151 17
 
0.1%
78.527865 17
 
0.1%
78.443391 16
 
0.1%
76.007429 16
 
0.1%
72.838778 16
 
0.1%
75.913139 16
 
0.1%
72.897808 16
 
0.1%
72.808778 16
 
0.1%
72.902658 16
 
0.1%
73.259083 16
 
0.1%
Other values (3407) 24566
99.3%
ValueCountFrequency (%)
72.778726 12
< 0.1%
72.778778 10
< 0.1%
72.781477 7
< 0.1%
72.782629 6
< 0.1%
72.782697 12
< 0.1%
72.784209 11
< 0.1%
72.788666 9
< 0.1%
72.788726 10
< 0.1%
72.788778 11
< 0.1%
72.791477 12
< 0.1%
ValueCountFrequency (%)
88.563452 2
< 0.1%
88.563187 3
< 0.1%
88.543452 1
 
< 0.1%
88.543187 3
< 0.1%
88.530581 3
< 0.1%
88.523452 2
< 0.1%
88.523294 2
< 0.1%
88.523187 2
< 0.1%
88.513452 3
< 0.1%
88.513187 3
< 0.1%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size902.4 KiB
Stormy
4222 
Fog
4170 
Cloudy
4122 
Windy
4093 
Sunny
4067 

Length

Max length10
Median length6
Mean length5.8198803
Min length3

Characters and Unicode

Total characters143914
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFog
2nd rowStormy
3rd rowSandstorms
4th rowFog
5th rowSandstorms

Common Values

ValueCountFrequency (%)
Stormy 4222
17.1%
Fog 4170
16.9%
Cloudy 4122
16.7%
Windy 4093
16.6%
Sunny 4067
16.4%
Sandstorms 4054
16.4%

Length

2023-04-23T17:45:39.247368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T17:45:39.600805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
stormy 4222
17.1%
fog 4170
16.9%
cloudy 4122
16.7%
windy 4093
16.6%
sunny 4067
16.4%
sandstorms 4054
16.4%

Most occurring characters

ValueCountFrequency (%)
o 16568
11.5%
y 16504
11.5%
n 16281
11.3%
S 12343
8.6%
d 12269
8.5%
r 8276
 
5.8%
m 8276
 
5.8%
t 8276
 
5.8%
u 8189
 
5.7%
s 8108
 
5.6%
Other values (7) 28824
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 119186
82.8%
Uppercase Letter 24728
 
17.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 16568
13.9%
y 16504
13.8%
n 16281
13.7%
d 12269
10.3%
r 8276
6.9%
m 8276
6.9%
t 8276
6.9%
u 8189
6.9%
s 8108
6.8%
g 4170
 
3.5%
Other values (3) 12269
10.3%
Uppercase Letter
ValueCountFrequency (%)
S 12343
49.9%
F 4170
 
16.9%
C 4122
 
16.7%
W 4093
 
16.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 143914
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 16568
11.5%
y 16504
11.5%
n 16281
11.3%
S 12343
8.6%
d 12269
8.5%
r 8276
 
5.8%
m 8276
 
5.8%
t 8276
 
5.8%
u 8189
 
5.7%
s 8108
 
5.6%
Other values (7) 28824
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 143914
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 16568
11.5%
y 16504
11.5%
n 16281
11.3%
S 12343
8.6%
d 12269
8.5%
r 8276
 
5.8%
m 8276
 
5.8%
t 8276
 
5.8%
u 8189
 
5.7%
s 8108
 
5.6%
Other values (7) 28824
20.0%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size902.4 KiB
Low
8078 
Jam
7970 
Medium
6240 
High
2440 

Length

Max length6
Median length3
Mean length3.8557101
Min length3

Characters and Unicode

Total characters95344
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJam
2nd rowHigh
3rd rowLow
4th rowJam
5th rowJam

Common Values

ValueCountFrequency (%)
Low 8078
32.7%
Jam 7970
32.2%
Medium 6240
25.2%
High 2440
 
9.9%

Length

2023-04-23T17:45:39.919724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T17:45:40.251503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
low 8078
32.7%
jam 7970
32.2%
medium 6240
25.2%
high 2440
 
9.9%

Most occurring characters

ValueCountFrequency (%)
m 14210
14.9%
i 8680
9.1%
L 8078
8.5%
o 8078
8.5%
w 8078
8.5%
J 7970
8.4%
a 7970
8.4%
M 6240
6.5%
e 6240
6.5%
d 6240
6.5%
Other values (4) 13560
14.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 70616
74.1%
Uppercase Letter 24728
 
25.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 14210
20.1%
i 8680
12.3%
o 8078
11.4%
w 8078
11.4%
a 7970
11.3%
e 6240
8.8%
d 6240
8.8%
u 6240
8.8%
g 2440
 
3.5%
h 2440
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
L 8078
32.7%
J 7970
32.2%
M 6240
25.2%
H 2440
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 95344
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 14210
14.9%
i 8680
9.1%
L 8078
8.5%
o 8078
8.5%
w 8078
8.5%
J 7970
8.4%
a 7970
8.4%
M 6240
6.5%
e 6240
6.5%
d 6240
6.5%
Other values (4) 13560
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 14210
14.9%
i 8680
9.1%
L 8078
8.5%
o 8078
8.5%
w 8078
8.5%
J 7970
8.4%
a 7970
8.4%
M 6240
6.5%
e 6240
6.5%
d 6240
6.5%
Other values (4) 13560
14.2%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size902.4 KiB
0
8279 
2
8230 
1
8219 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24728
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row0
4th row1
5th row2

Common Values

ValueCountFrequency (%)
0 8279
33.5%
2 8230
33.3%
1 8219
33.2%

Length

2023-04-23T17:45:40.512775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T17:45:40.797735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 8279
33.5%
2 8230
33.3%
1 8219
33.2%

Most occurring characters

ValueCountFrequency (%)
0 8279
33.5%
2 8230
33.3%
1 8219
33.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24728
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8279
33.5%
2 8230
33.3%
1 8219
33.2%

Most occurring scripts

ValueCountFrequency (%)
Common 24728
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8279
33.5%
2 8230
33.3%
1 8219
33.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24728
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8279
33.5%
2 8230
33.3%
1 8219
33.2%

Type_of_order
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size902.4 KiB
Meal
6270 
Snack
6214 
Drinks
6208 
Buffet
6036 

Length

Max length6
Median length5
Mean length5.2415885
Min length4

Characters and Unicode

Total characters129614
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSnack
2nd rowMeal
3rd rowBuffet
4th rowSnack
5th rowBuffet

Common Values

ValueCountFrequency (%)
Meal 6270
25.4%
Snack 6214
25.1%
Drinks 6208
25.1%
Buffet 6036
24.4%

Length

2023-04-23T17:45:41.082503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T17:45:41.457557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
meal 6270
25.4%
snack 6214
25.1%
drinks 6208
25.1%
buffet 6036
24.4%

Most occurring characters

ValueCountFrequency (%)
a 12484
 
9.6%
n 12422
 
9.6%
k 12422
 
9.6%
e 12306
 
9.5%
f 12072
 
9.3%
M 6270
 
4.8%
l 6270
 
4.8%
S 6214
 
4.8%
c 6214
 
4.8%
D 6208
 
4.8%
Other values (6) 36732
28.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 104886
80.9%
Uppercase Letter 24728
 
19.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 12484
11.9%
n 12422
11.8%
k 12422
11.8%
e 12306
11.7%
f 12072
11.5%
l 6270
6.0%
c 6214
5.9%
r 6208
5.9%
i 6208
5.9%
s 6208
5.9%
Other values (2) 12072
11.5%
Uppercase Letter
ValueCountFrequency (%)
M 6270
25.4%
S 6214
25.1%
D 6208
25.1%
B 6036
24.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 129614
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 12484
 
9.6%
n 12422
 
9.6%
k 12422
 
9.6%
e 12306
 
9.5%
f 12072
 
9.3%
M 6270
 
4.8%
l 6270
 
4.8%
S 6214
 
4.8%
c 6214
 
4.8%
D 6208
 
4.8%
Other values (6) 36732
28.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 12484
 
9.6%
n 12422
 
9.6%
k 12422
 
9.6%
e 12306
 
9.5%
f 12072
 
9.3%
M 6270
 
4.8%
l 6270
 
4.8%
S 6214
 
4.8%
c 6214
 
4.8%
D 6208
 
4.8%
Other values (6) 36732
28.3%

Type_of_vehicle
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size902.4 KiB
motorcycle
14567 
scooter
8233 
electric_scooter
1928 

Length

Max length16
Median length10
Mean length9.4689825
Min length7

Characters and Unicode

Total characters234149
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmotorcycle
2nd rowmotorcycle
3rd rowmotorcycle
4th rowscooter
5th rowelectric_scooter

Common Values

ValueCountFrequency (%)
motorcycle 14567
58.9%
scooter 8233
33.3%
electric_scooter 1928
 
7.8%

Length

2023-04-23T17:45:41.769830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T17:45:42.129740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
motorcycle 14567
58.9%
scooter 8233
33.3%
electric_scooter 1928
 
7.8%

Most occurring characters

ValueCountFrequency (%)
o 49456
21.1%
c 43151
18.4%
e 28584
12.2%
t 26656
11.4%
r 26656
11.4%
l 16495
 
7.0%
m 14567
 
6.2%
y 14567
 
6.2%
s 10161
 
4.3%
i 1928
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 232221
99.2%
Connector Punctuation 1928
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 49456
21.3%
c 43151
18.6%
e 28584
12.3%
t 26656
11.5%
r 26656
11.5%
l 16495
 
7.1%
m 14567
 
6.3%
y 14567
 
6.3%
s 10161
 
4.4%
i 1928
 
0.8%
Connector Punctuation
ValueCountFrequency (%)
_ 1928
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 232221
99.2%
Common 1928
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 49456
21.3%
c 43151
18.6%
e 28584
12.3%
t 26656
11.5%
r 26656
11.5%
l 16495
 
7.1%
m 14567
 
6.3%
y 14567
 
6.3%
s 10161
 
4.4%
i 1928
 
0.8%
Common
ValueCountFrequency (%)
_ 1928
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 234149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 49456
21.1%
c 43151
18.4%
e 28584
12.2%
t 26656
11.4%
r 26656
11.4%
l 16495
 
7.0%
m 14567
 
6.2%
y 14567
 
6.2%
s 10161
 
4.3%
i 1928
 
0.8%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size902.4 KiB
1.0
15635 
0.0
7766 
2.0
 
1129
3.0
 
198

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters74184
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 15635
63.2%
0.0 7766
31.4%
2.0 1129
 
4.6%
3.0 198
 
0.8%

Length

2023-04-23T17:45:42.608081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T17:45:43.000447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 15635
63.2%
0.0 7766
31.4%
2.0 1129
 
4.6%
3.0 198
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 32494
43.8%
. 24728
33.3%
1 15635
21.1%
2 1129
 
1.5%
3 198
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49456
66.7%
Other Punctuation 24728
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32494
65.7%
1 15635
31.6%
2 1129
 
2.3%
3 198
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 24728
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 74184
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32494
43.8%
. 24728
33.3%
1 15635
21.1%
2 1129
 
1.5%
3 198
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74184
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32494
43.8%
. 24728
33.3%
1 15635
21.1%
2 1129
 
1.5%
3 198
 
0.3%

Festival
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size733.4 KiB
False
24235 
True
 
493
ValueCountFrequency (%)
False 24235
98.0%
True 493
 
2.0%
2023-04-23T17:45:43.284588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

City
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size902.4 KiB
Metropolitian
19053 
Urban
5576 
Semi-Urban
 
99

Length

Max length13
Median length13
Mean length11.184042
Min length5

Characters and Unicode

Total characters276559
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMetropolitian
2nd rowMetropolitian
3rd rowMetropolitian
4th rowMetropolitian
5th rowMetropolitian

Common Values

ValueCountFrequency (%)
Metropolitian 19053
77.1%
Urban 5576
 
22.5%
Semi-Urban 99
 
0.4%

Length

2023-04-23T17:45:43.540407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T17:45:43.860964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
metropolitian 19053
77.1%
urban 5576
 
22.5%
semi-urban 99
 
0.4%

Most occurring characters

ValueCountFrequency (%)
i 38205
13.8%
t 38106
13.8%
o 38106
13.8%
r 24728
8.9%
a 24728
8.9%
n 24728
8.9%
e 19152
6.9%
M 19053
6.9%
p 19053
6.9%
l 19053
6.9%
Other values (5) 11647
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 251633
91.0%
Uppercase Letter 24827
 
9.0%
Dash Punctuation 99
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 38205
15.2%
t 38106
15.1%
o 38106
15.1%
r 24728
9.8%
a 24728
9.8%
n 24728
9.8%
e 19152
7.6%
p 19053
7.6%
l 19053
7.6%
b 5675
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
M 19053
76.7%
U 5675
 
22.9%
S 99
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 99
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 276460
> 99.9%
Common 99
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 38205
13.8%
t 38106
13.8%
o 38106
13.8%
r 24728
8.9%
a 24728
8.9%
n 24728
8.9%
e 19152
6.9%
M 19053
6.9%
p 19053
6.9%
l 19053
6.9%
Other values (4) 11548
 
4.2%
Common
ValueCountFrequency (%)
- 99
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 276559
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 38205
13.8%
t 38106
13.8%
o 38106
13.8%
r 24728
8.9%
a 24728
8.9%
n 24728
8.9%
e 19152
6.9%
M 19053
6.9%
p 19053
6.9%
l 19053
6.9%
Other values (5) 11647
 
4.2%

Time_taken (min)
Real number (ℝ)

Distinct45
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.593699
Minimum10
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size902.4 KiB
2023-04-23T17:45:44.170329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile13
Q119
median26
Q333
95-th percentile44
Maximum54
Range44
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.3327623
Coefficient of variation (CV)0.35093885
Kurtosis-0.35613053
Mean26.593699
Median Absolute Deviation (MAD)7
Skewness0.45788754
Sum657609
Variance87.100453
MonotonicityNot monotonic
2023-04-23T17:45:44.535049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
26 1174
 
4.7%
25 1118
 
4.5%
28 1088
 
4.4%
27 1080
 
4.4%
29 1067
 
4.3%
15 955
 
3.9%
18 952
 
3.8%
24 922
 
3.7%
19 921
 
3.7%
23 914
 
3.7%
Other values (35) 14537
58.8%
ValueCountFrequency (%)
10 343
 
1.4%
11 360
 
1.5%
12 351
 
1.4%
13 391
1.6%
14 357
 
1.4%
15 955
3.9%
16 901
3.6%
17 912
3.7%
18 952
3.8%
19 921
3.7%
ValueCountFrequency (%)
54 47
 
0.2%
53 57
 
0.2%
52 37
 
0.1%
51 42
 
0.2%
50 39
 
0.2%
49 148
0.6%
48 155
0.6%
47 154
0.6%
46 166
0.7%
45 130
0.5%

Time_Orderd_hour
Real number (ℝ)

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.530249
Minimum8
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size902.4 KiB
2023-04-23T17:45:44.837717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9
Q115
median19
Q321
95-th percentile23
Maximum23
Range15
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.4705322
Coefficient of variation (CV)0.25501818
Kurtosis-0.51703344
Mean17.530249
Median Absolute Deviation (MAD)2
Skewness-0.81100829
Sum433488
Variance19.985658
MonotonicityNot monotonic
2023-04-23T17:45:45.112794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
21 2724
11.0%
17 2696
10.9%
19 2646
10.7%
18 2646
10.7%
20 2600
10.5%
22 2560
10.4%
23 2225
9.0%
10 1142
 
4.6%
11 1122
 
4.5%
8 1088
 
4.4%
Other values (6) 3279
13.3%
ValueCountFrequency (%)
8 1088
4.4%
9 1063
 
4.3%
10 1142
4.6%
11 1122
4.5%
12 444
 
1.8%
13 430
 
1.7%
14 444
 
1.8%
15 480
 
1.9%
16 418
 
1.7%
17 2696
10.9%
ValueCountFrequency (%)
23 2225
9.0%
22 2560
10.4%
21 2724
11.0%
20 2600
10.5%
19 2646
10.7%
18 2646
10.7%
17 2696
10.9%
16 418
 
1.7%
15 480
 
1.9%
14 444
 
1.8%

Time_Orderd_minute
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.478203
Minimum10
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size902.4 KiB
2023-04-23T17:45:45.434684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q120
median30
Q340
95-th percentile55
Maximum55
Range45
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.626238
Coefficient of variation (CV)0.44708143
Kurtosis-1.0592838
Mean30.478203
Median Absolute Deviation (MAD)10
Skewness0.15122836
Sum753665
Variance185.67437
MonotonicityNot monotonic
2023-04-23T17:45:45.721585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
35 2948
11.9%
40 2812
11.4%
20 2775
11.2%
10 2745
11.1%
25 2723
11.0%
30 2702
10.9%
15 2702
10.9%
45 1834
7.4%
50 1785
7.2%
55 1702
6.9%
ValueCountFrequency (%)
10 2745
11.1%
15 2702
10.9%
20 2775
11.2%
25 2723
11.0%
30 2702
10.9%
35 2948
11.9%
40 2812
11.4%
45 1834
7.4%
50 1785
7.2%
55 1702
6.9%
ValueCountFrequency (%)
55 1702
6.9%
50 1785
7.2%
45 1834
7.4%
40 2812
11.4%
35 2948
11.9%
30 2702
10.9%
25 2723
11.0%
20 2775
11.2%
15 2702
10.9%
10 2745
11.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size902.4 KiB
22
24728 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters49456
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22
2nd row22
3rd row22
4th row22
5th row22

Common Values

ValueCountFrequency (%)
22 24728
100.0%

Length

2023-04-23T17:45:46.024985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T17:45:46.299462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
22 24728
100.0%

Most occurring characters

ValueCountFrequency (%)
2 49456
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49456
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 49456
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49456
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 49456
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 49456
100.0%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size902.4 KiB
10
24728 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters49456
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10 24728
100.0%

Length

2023-04-23T17:45:46.527487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-23T17:45:46.786528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
10 24728
100.0%

Most occurring characters

ValueCountFrequency (%)
1 24728
50.0%
0 24728
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49456
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 24728
50.0%
0 24728
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49456
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 24728
50.0%
0 24728
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 24728
50.0%
0 24728
50.0%

Interactions

2023-04-23T17:45:29.962615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:04.445555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:08.177411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:11.085056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:14.093594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:17.259024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:20.075644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:23.210192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:26.195825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:30.310518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:04.781999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:08.548807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:11.433941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:14.468523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:17.595343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:20.432900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:23.543793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:26.591630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:30.643649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:05.117842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:08.856403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:11.758746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:14.796833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:17.895357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:20.777455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:23.862515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:26.951159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:31.000654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:05.475319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:09.191683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:12.098546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:15.181798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:18.226910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:21.138427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:24.225682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:27.347372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:31.366668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:05.835397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:09.525770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:12.454837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:15.557982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:18.552686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:21.497428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:24.571439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:27.738650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:31.695652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:06.833407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:09.821709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:12.761319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:15.893390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:18.833735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:21.824986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:24.879636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:28.503483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:32.063073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:07.197687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:10.160117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:13.117496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:16.260541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:19.175591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:22.186466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:25.266571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:28.898033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:32.393000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:07.531167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:10.487514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:13.456745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:16.612270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:19.486417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:22.539607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:25.576697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:29.251447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:32.742577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:07.897465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:10.833569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:13.825794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:16.979028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:19.825458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:22.936489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:25.917323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-23T17:45:29.648983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-23T17:45:47.061774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Delivery_person_AgeDelivery_person_RatingsRestaurant_latitudeRestaurant_longitudeDelivery_location_latitudeDelivery_location_longitudeTime_taken (min)Time_Orderd_hourTime_Orderd_minuteWeather_conditionsRoad_traffic_densityVehicle_conditionType_of_orderType_of_vehiclemultiple_deliveriesFestivalCity
Delivery_person_Age1.000-0.1040.0040.0130.0040.0130.3050.0040.0000.0140.0000.0000.0000.0000.0740.0680.059
Delivery_person_Ratings-0.1041.000-0.0020.004-0.006-0.001-0.301-0.0280.0070.0880.0880.1030.0000.0630.0960.0760.055
Restaurant_latitude0.004-0.0021.000-0.1460.997-0.1450.0120.0080.0020.0000.0070.0000.0000.0070.0050.0000.000
Restaurant_longitude0.0130.004-0.1461.000-0.1480.9960.010-0.0060.0040.0000.0070.0000.0000.0000.0100.0000.000
Delivery_location_latitude0.004-0.0060.997-0.1481.000-0.1420.0310.0410.0020.0030.0130.0000.0050.0010.0000.0000.000
Delivery_location_longitude0.013-0.001-0.1450.996-0.1421.0000.0320.0320.0030.0000.0530.0000.0000.0000.0140.0160.012
Time_taken (min)0.305-0.3010.0120.0100.0310.0321.0000.0970.0100.1380.2610.2240.0000.1300.3350.4220.287
Time_Orderd_hour0.004-0.0280.008-0.0060.0410.0320.0971.000-0.0300.0001.0000.0000.0080.0000.1070.1230.078
Time_Orderd_minute0.0000.0070.0020.0040.0020.0030.010-0.0301.0000.0080.0290.0000.0000.0000.0000.0000.000
Weather_conditions0.0140.0880.0000.0000.0030.0000.1380.0000.0081.0000.0000.0000.0000.0000.0660.0630.041
Road_traffic_density0.0000.0880.0070.0070.0130.0530.2611.0000.0290.0001.0000.0090.0000.0000.1030.1200.076
Vehicle_condition0.0000.1030.0000.0000.0000.0000.2240.0000.0000.0000.0091.0000.0060.4930.0930.1030.067
Type_of_order0.0000.0000.0000.0000.0050.0000.0000.0080.0000.0000.0000.0061.0000.0000.0030.0080.000
Type_of_vehicle0.0000.0630.0070.0000.0010.0000.1300.0000.0000.0000.0000.4930.0001.0000.0580.0570.039
multiple_deliveries0.0740.0960.0050.0100.0000.0140.3350.1070.0000.0660.1030.0930.0030.0581.0000.2020.137
Festival0.0680.0760.0000.0000.0000.0160.4220.1230.0000.0630.1200.1030.0080.0570.2021.0000.116
City0.0590.0550.0000.0000.0000.0120.2870.0780.0000.0410.0760.0670.0000.0390.1370.1161.000

Missing values

2023-04-23T17:45:33.233747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-23T17:45:34.223605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Delivery_person_AgeDelivery_person_RatingsRestaurant_latitudeRestaurant_longitudeDelivery_location_latitudeDelivery_location_longitudeWeather_conditionsRoad_traffic_densityVehicle_conditionType_of_orderType_of_vehiclemultiple_deliveriesFestivalCityTime_taken (min)Time_Orderd_hourTime_Orderd_minuteTime_Order_picked_hourTime_Order_picked_minute
036.04.230.32796878.04610630.39796878.116106FogJam2Snackmotorcycle3.0NoMetropolitian4621552210
121.04.710.00306476.30758910.04306476.347589StormyHigh1Mealmotorcycle1.0NoMetropolitian2314552210
334.04.330.89958475.80934630.91958475.829346SandstormsLow0Buffetmotorcycle0.0NoMetropolitian2009202210
424.04.726.46350480.37292926.59350480.502929FogJam1Snackscooter1.0NoMetropolitian4119502210
529.04.519.17626972.83672119.26626972.926721SandstormsJam2Buffetelectric_scooter1.0NoMetropolitian2020252210
635.04.012.31107276.65487812.35107276.694878WindyHigh1Mealscooter1.0NoMetropolitian3314552210
733.04.218.59271873.77357218.70271873.883572SandstormsJam2Snackmotorcycle1.0NoMetropolitian4020302210
921.04.722.55267288.35288522.58267288.382885WindyJam0Mealmotorcycle1.0NoUrban1521152210
1025.04.118.56393473.91536718.64393573.995367SandstormsJam0Snackmotorcycle2.0NoMetropolitian3620202210
1131.04.723.35780485.32514623.48780485.455146SandstormsLow2Mealelectric_scooter0.0NoMetropolitian2622302210
Delivery_person_AgeDelivery_person_RatingsRestaurant_latitudeRestaurant_longitudeDelivery_location_latitudeDelivery_location_longitudeWeather_conditionsRoad_traffic_densityVehicle_conditionType_of_orderType_of_vehiclemultiple_deliveriesFestivalCityTime_taken (min)Time_Orderd_hourTime_Orderd_minuteTime_Order_picked_hourTime_Order_picked_minute
4556521.04.819.10324972.84674919.13324972.876749SunnyLow2Drinksmotorcycle0.0NoMetropolitian2222152210
4556930.04.911.02508377.01539311.04508377.035393WindyLow1Snackmotorcycle1.0NoMetropolitian2610252210
4557220.04.921.18660872.79413621.21660872.824136SandstormsJam2Drinksmotorcycle1.0NoUrban1820252210
4557436.04.812.31097276.65926412.44097276.789264SunnyJam2Drinkselectric_scooter1.0NoUrban2921102210
4557537.04.813.02239480.24243913.04239480.262439SandstormsLow2Drinkselectric_scooter0.0NoMetropolitian2009352210
4557630.04.226.46900380.31634426.53900380.386344CloudyMedium1Snackmotorcycle2.0YesMetropolitian4218102210
4557835.04.223.37129285.32787223.48129285.437872WindyJam2Drinksmotorcycle1.0NoMetropolitian3321452210
4557930.04.826.90232875.79425726.91232875.804257WindyHigh1Mealmotorcycle0.0NoMetropolitian3211352210
4558220.04.711.00175376.98624111.04175377.026241CloudyHigh0Snackmotorcycle1.0NoMetropolitian2613352210
4558323.04.923.35105885.32573123.43105885.405731FogMedium2Snackscooter1.0NoMetropolitian3617102210